System and Method For Artificial Lift System Analysis

ABSTRACT

A system, method, and computer program product are disclosed for analysis of artificial lift systems. An Artificial Lift Analysis Solution (ALAS) is also provided to view and analyze artificial lift well data trends, prediction and detection event alerts, and to diagnose system conditions to facilitate production optimization. Production well information is provided for a plurality of the production wells each being associated with an artificial lift system. Artificial lift system failure alerts for the plurality of production wells are received and processed on a computer. A relevance measure for each of the artificial lift system failure alerts is determined responsive to the production well information. A summary of the artificial lift system failure alerts is displayed in an ordering based on the relevance measure.

CROSS-REFERENCE TO A RELATED APPLICATION

The present application for patent claims the benefit of U.S.provisional patent application bearing Ser. No. 61/581,432, filed onDec. 29, 2011, which is incorporated by reference in its entirety.

TECHNICAL FIELD

This invention relates to artificial lift systems in oil field assets,and more particularly, to a system, method, and computer program productfor analysis of artificial lift systems.

BACKGROUND

Artificial lift systems are widely used to enhance production forreservoirs with formation pressure too low to provide enough energy todirectly lift fluids to the surface. Examples of artificial lift systemsinclude gas lift systems, hydraulic pumping units, electric submersiblepumps (ESPs), progressive cavity pumps (PCPs), plunger lift systems, andsucker rod pump systems. Sucker rod pumps are currently the mostcommonly used artificial lift system in the industry.

There are many currently available operational tools and softwareprograms used to monitor, evaluate, and optimize the performance ofartificial lift systems. For example, pump off controllers (POCs), whichgather and record periodic measurements indicative of an artificial liftsystem's operational status, play a significant role in monitoring theoperation of artificial lift pumping systems, such as rod pumps.Additionally, POCs can be programmed to automatically shut down units ifthe values of torque and load deviate beyond thresholds. While POCs helpreduce the amount of work required by the production and maintenancepersonnel operating in the field, the POCs by themselves are notsufficient. In particular, the dataset obtained by POCs poses difficultchallenges due to high dimensionality, noise, and inadequate labeling.

The vast amounts of data collected (or calculated by the system) oftencannot be processed in a time frame to determine what actions are neededto prevent system failures and/or improve the performance of theartificial lift well. The operating state of the artificial lift systemis frequently diagnosed incorrectly, thus resulting in increased downtime and reduced recovery rates. In particular, there are ofteninterdependencies between the multiple parameters collected, which makeit difficult for an automated (expert) system to evaluate the complexdynamic situations of an artificial lift system. For example, ifoperating parameters A, B, and C are collected where A is within normaloperational conditions but B and C are outside of normal operationalconditions, the system may suggest the issuance of an alert or shutdownbased on the observed data signature. While there may be a need topreemptively shut down or service the artificial lift system based onthe aggregate state of various operating parameters, in some cases itmay be acceptable or even advantageous to continue operating the asseteven if it is in a degraded mode of operation.

Expert systems, which use rule-based decisions, have been developed tobetter evaluate operational conditions of artificial lift systems.However, such expert systems may not perform as well as needed if acomplete set of data required for making a proper diagnosis is notavailable. Such expert systems may also incorrectly diagnose liftingproblems if the artificial lift systems are not regularly recalibrated,particularly because the parameters of operation change during the lifeof the well.

There is a need for automated systems, such as artificial intelligentsystems that can dynamically keep track of various parameters in eachand every unit, give early indications or warnings of failures, andprovide suggestions on types of maintenance work required based on theknowledge acquired from previous best practices. Such a system shouldalso display information in a useful manner such that a subject matterexpert (SME) can quickly review each artificial lift system in an oilfield and implement changes if necessary. Such a system would be asignificant asset to the petroleum industry, especially for use in oilfields having hundreds or thousands of wells where the availability ofSMEs may be limited.

SUMMARY

A method for artificial lift system analysis is disclosed. The methodcomprises providing production well information for a plurality of theproduction wells each being associated with an artificial lift system.Artificial lift system failure alerts for the plurality of productionwells are received and processed on a computer. A relevance measure foreach of the artificial lift system failure alerts is determinedresponsive to the production well information. A summary of theartificial lift system failure alerts is displayed in an ordering basedon the relevance measure.

A system for artificial lift system analysis is also disclosed. Thesystem comprises a database, a computer processor, a computer program,and a display. The database is configured to store production wellinformation for a plurality of production wells each being associatedwith an artificial lift system. The computer program is executable onthe computer processor and comprises an Artificial Lift AnalysisSolution. The Artificial Lift Analysis Solution processes artificiallift system failure alerts for the plurality of production wells,determines a relevance measure for each of the artificial lift systemfailure alerts responsive to the production well information, and ordersthe artificial lift system failure alerts using the relevance measure.The display communicates with the computer program to produce image datarepresentations of the artificial lift system failure alerts orderedusing the relevance measure.

In one or more embodiments, the Artificial Lift Analysis Solutionfurther comprises a well history module that generates a well historygraph for one or more of the plurality of production wells.

In one or more embodiments, the Artificial Lift Analysis Solutionfurther comprises an alert history module that generates an alerthistory graph for one or more of the plurality of production wells.

In one or more embodiments, the Artificial Lift Analysis Solutionfurther comprises a well neighborhood module that generates a comparisonof production performance for one or more of the plurality of productionwells with one or more associated neighborhood wells.

In one or more embodiments, the Artificial Lift Analysis Solutionfurther comprises a failure alerts management module that correctsartificial lift system failure alerts to train an artificialintelligence engine.

In one or more embodiments, the Artificial Lift. Analysis Solutionfurther comprises a dynamometer card history module that generates adynamometer card history for one or more of the plurality of productionwells associated with rod pump artificial lift systems.

A non-transitory processor readable medium containing computer readablesoftware instructions used for surveillance of artificial lift systemsis also disclosed. The computer readable instructions compriseprocessing production well information and artificial lift systemfailure alerts for a plurality of production wells each being associatedwith an artificial lift system, determining a relevance measure for eachof the artificial lift system failure alerts responsive to theproduction well information, and ordering the artificial lift systemfailure alerts using the relevance measure.

In one or more embodiments, the artificial lift system failure alertscomprise predicted failure alerts and detected failure alerts. Therelevance measure can comprise an urgency of the artificial lift systemfailure alerts such that the detected failure alerts are ordered beforethe predicted failure alerts in the summary of the artificial liftsystem failure alerts.

In one or more embodiments, the relevance measure comprises acombination of average daily production and potential production volumeloss for each of the plurality of production wells.

In one or more embodiments, the relevance measure comprises anartificial lift failure type.

In one or more embodiments, the relevance measure comprises a completionstatus such that the artificial lift system failure alerts are orderedaccording to whether the alert is associated with a new, underevaluation, in progress, or closed completion status.

In one or more embodiments, one or more of the plurality of productionwells are selected. A well history graph can be generated for the one ormore of the plurality of production wells selected. An alert historygraph can be generated for the one or more of the plurality ofproduction wells selected. A comparison of production performance can begenerated for the one or more of the plurality of production wellsselected with one or more associated neighborhood wells.

In one or more embodiments, one or more of the plurality of productionwells that are associated with rod pump artificial lift systems areselected. A dynamometer card history is generated for the one or more ofthe plurality of production wells selected.

In one or more embodiments, an artificial lift system failure alert thatis incorrect is identified, corrected, and an artificial intelligenceengine is trained with the corrected artificial lift system failurealert.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example screenshot of the Artificial LiftSurveillance Tool, according to an embodiment of the present invention.

FIG. 1A shows an example header of the Artificial Lift Surveillance Toolas the “group by” dropdown list is adjusted, according to an embodimentof the present invention.

FIGS. 1B-1E show various views of the data body of the Artificial LiftSurveillance Tool when different example hierarchy structures areselected, according to embodiments of the present invention.

FIG. 1F shows an example header of the Artificial Lift Surveillance Toolas the “size by” dropdown list is adjusted, according to an embodimentof the present invention.

FIG. 1G shows a view of the data body of the Artificial LiftSurveillance Tool as the “size by” dropdown list is adjusted, accordingto an embodiment of the present invention.

FIG. 1H shows an example header of the Artificial Lift Surveillance Toolas the “Colors show changes since” dropdown list is adjusted, accordingto an embodiment of the present invention.

FIGS. 2A and 2B illustrate example tooltips displayed in the ArtificialLift Surveillance Tool, according to embodiments of the presentinvention.

FIG. 3 illustrates example production and equipment filters for a rodpump system in the Artificial Lift Surveillance Tool, according to anembodiment of the present invention.

FIG. 4 illustrates an example of how artificial lift wells can besearched in the Artificial Lift Surveillance Tool, according to anembodiment of the present invention.

FIGS. 5A and 5B illustrate an example screenshot of the Artificial LiftAnalysis Solution Dashboard, according to an embodiment of the presentinvention.

FIGS. 6A and 6B illustrate an example screenshot of the Artificial LiftAnalysis Solution Failure Prediction Module, according to an embodimentof the present invention.

FIGS. 7A and 7B show examples of managing alerts in the Artificial LiftAnalysis Solution, according to embodiments of the present invention.

FIGS. 8A and 8B illustrate an example screenshot of the Artificial LiftAnalysis Solution Neighborhood Wells Module, according to an embodimentof the present invention.

FIG. 9 illustrates an example screenshot of the Artificial Lift AnalysisSolution Dyno Card Well Listing Module, according to an embodiment ofthe present invention.

FIG. 10 illustrates an example screenshot of the Artificial LiftAnalysis Solution Dyno Card History Module, according to an embodimentof the present invention.

FIG. 11 shows a system for surveillance, analysis, and management ofartificial lift systems, according to embodiments of the presentinvention.

DETAILED DESCRIPTION

Embodiments of the present invention relate to artificial lift systemfailures in oil field assets, which lead to production loss and cangreatly increase operational expenditures. In particular, systems,methods, and computer program products are disclosed for surveillance,analysis, and management of artificial lift system performance.Predicting artificial lift system failures can dramatically improveperformance, such as by adjusting operating parameters to forestallfailures or by scheduling maintenance to reduce unplanned repairs andminimize downtime. For brevity, the below description is largelydescribed in relation to sucker rod pumps. However, embodiments of thepresent invention can also be applied to other types of artificial liftsystems such as gas lift systems, hydraulic pumping units, electricsubmersible pumps (ESPs), progressive cavity pumps (PCPs), and plungerlift systems.

In one embodiment, an Artificial Lift Surveillance Tool (ALST) isprovided. In another embodiment, an Artificial Lift Analysis Solution(ALAS) is provided. In another embodiment, an Artificial Lift ManagementTool (ALMT) is provided. As will be described, these tools can be usedfor reducing the amount of time it takes to identify an artificial liftwell with a problem (i.e., identify a production exception) andattribute the problem to the artificial lift system (i.e., analyze theproblem). The Artificial Lift Surveillance Tool, Artificial LiftAnalysis Solution, and Artificial Lift Management Tool integratenumerous types of data and pull it into a single environment, which inturn helps to reduce analysis time. In particular, they integratesurveillance, analysis, artificial intelligence, alerts and escalations,and reporting; thereby improving the efficiency with which wells aremonitored, catching exceptions early, tracking field work from beginningto end, and reducing the time and costs. This ultimately reducesdowntime and lost production due to inefficiencies.

As will be described, the Artificial Lift Surveillance Tool can be usedto proactively identify problems that will likely have the largestnegative impact on production (e.g., identify producing wells that areexperiencing large production declines). It incorporates key artificiallift metrics to help attribute production declines to common problemsthat occur with artificial lift systems. The Artificial LiftSurveillance Tool uses Treemap technology, which takes a large amount ofdata and puts it in an easy-to-review format. In particular, ALSTorganizes field information to focus on artificial lift systems thatshould further be investigated by a subject matter expert, therebymanaging by exception. The Artificial Lift. Analysis Solution, whichtypically provides a larger history of production and artificial liftsystem data, can be used by subject matter experts for investigatingartificial lift systems. In particular, ALAS can be used to determinethe root cause of problems and fully understand well performanceimprovement opportunities. The Artificial Lift Management Tool can beused to reduce the time needed to respond to artificial lift wellfailures in the field and to reduce downtime and lost production due toinefficiencies. In particular, the ALMT serves as a collaborative toolallowing operations control personnel to track well work from the time afailure alert is surfaced to the time a ticket is closed (if a ticketwas opened as a result of the alert to fix the well).

FIG. 1 shows an example screenshot of the Artificial Lift SurveillanceTool. In FIG. 1, data body 101 displays individual artificial liftwells, a grouping of artificial lift wells, or both. Each individual boxrepresents a single artificial lift well that can be grouped based onthe user's preference. For example, in one embodiment, grouping can beby section, gauger beat, or manifold. Header 103 provides additionalselection dropdowns that can be used to customize the view of data body101. For example, data body 101 can be customized using header 103 basedon equipment type and the operations supervisor for a given area oroil/gas field, as well as, “group by” and “size by” selection dropdowns.Customization can also be made based on recent changes in fluidproduction using the “colors show changes since” dropdown list. Thecolor of each artificial lift well in data body 101 is automaticallyupdated based on the time frame selected in the “colors show changessince” dropdown list. A legend explaining the color variances can beprovided in header 103 as well. In FIG. 1, various cross-hatchingpatterns are used to represent different colors. However, one skilled inthe art will appreciate that different colors, backgrounds, patterns,shades, textures, or other markings can be used to distinguish betweendifferent values of attributes (e.g., change in production rate).

As will be described, the description or tooltip 105 highlights detailedinformation for an artificial lift well selected in data body 101,including production data and equipment specifications. Filters 107allow the user to filter out artificial lift wells according to generalproduction metrics, as well as, metrics specific to each artificial liftmethod. Well identification information for each of the artificial liftwells in data body 101 is not shown in FIG. 1; however, in someembodiments, well identification information is displayed in each cellrepresenting an artificial lift well. For example, well names aredisplayed in each cell of data body 101 in FIG. 4. If a cell is toosmall to display well identification information, in some embodiments, apop-up window containing the well identification information isdisplayed when the well is selected or hovered over, such as by acomputer mouse.

FIG. 1A shows an enlarged view of header 103. As will be described,header 103 can include one or more of equipment type dropdown list 109,operations supervisor dropdown list 111, “group by” dropdown list 113,“size by” dropdown list 115, “Colors show changes since” dropdown list117, color variances legend 119, a manage state button 121, an exportdata button 123, a saved view dropdown list 125, and a refresh button127. In FIG. 1A, equipment type dropdown list 109 allows the user toselect the type of artificial lift system to be viewed in data body 101.For example, gas lift systems, hydraulic pumping units, electricsubmersible pumps (ESPs), progressive cavity pumps (PCPs), plunger liftsystems, and beam/rod pump systems can be selected. In some embodiments,more than one type of artificial lift system can be selected for viewingin data body 101. In one embodiment, the equipment type dropdown isdefault to a particular artificial lift system, such as beam/rod pump.In another embodiment, the dropdown list is ordered alphabetically. Theoperations supervisor dropdown list 111 is used to switch betweenoperations supervisor areas or oil/gas fields. Accordingly, the user canquickly navigate between different assets using the operationssupervisor dropdown list 111. The “group by” dropdown list 113 allowsthe user to switch between various hierarchy structures. For example, inthe embodiment shown in FIG. 1A. the “group by” dropdown list 113 allowsthe user to navigate between section, gauger beat, or manifoldhierarchies. The user can also select no grouping. In each of theseinstances, the user can drill down to the artificial lift well level.

FIGS. 1B-1E show various views in data body 101 when different hierarchystructures are selected in the “group by” dropdown, list 113. Inparticular, FIG. 1B is a display of group by section. Here, the view indata body 101 displays all artificial lift wells partitioned into foursections (i.e., Sections 198, 197, 196, and 200). Although, there arefour sections in this example, other fields/assets may have a smaller orlarger quantity of sections. FIG. 1C is a display of group by gaugerbeat. Here, the view in data body 101 displays all artificial lift wellspartitioned into two gauger beats (i.e., gauger beat #1 OIL and #2 OIL).Although, there are two gauger beats in this example, otherfields/assets may have a smaller or larger quantity of gauger beats.FIG. 1D is a display of group by manifold. Here, the view in data body101 displays all artificial lift wells partitioned into six manifolds(i.e., manifolds CVU-P5, NVAWU-P2, VGSAU BP3, CVU-P4, WVU-P, andNQOBA-P). Although, there are six manifolds in this example, otherfields/assets may have a smaller or larger quantity of manifolds. FIG.1E is a display showing no grouping hierarchies, as such all individualartificial lift wells in the asset are shown in this figure. In each ofFIGS. 1B-1E the cross-hatched boxes represent individual artificial liftwells in the asset. It is important to note that the number of sections,gauger beats and manifolds is dependent on the field that is beingdisplayed in ALST. For example, field “A” might be partitioned into 4sections while field “B” might be partitioned into 7 sections. Thus, thenumber of sections displayed in data body 101 when “sections” isselected in the “group by” dropdown list 113 depends on the field/assetselected in operations supervisor dropdown list 111 of header 103 (i.e.,4 sections for field “A” and 7 sections for field “B”).

FIGS. 1F and 1G show screenshots as the “size by” dropdown list 115 ofheader 103 is adjusted. As shown in FIG. 1F, in accordance with anembodiment of the present invention, users can adjust the size betweennet oil, net gas, net water, or gross production. For each of theseoptions, the user can also select a time period for viewing, such as 90days or 180 days. These predetermined time periods can be adjusted basedon the user's preferences. As shown in FIG. 1G, artificial lift wellsare grouped from largest producer (top left) to smallest producer(bottom right) in data body 101 according to the selection picked in the“size by” dropdown list 115.

FIG. 1H shows a screenshot as the “Colors show changes since” dropdownlist 117 of header 103 is adjusted. As shown in FIG. 1H, in accordancewith an embodiment of the present invention, users can display theamount of change for oil, gas, or water production. For each of theseoptions, the user can also select a time period for viewing, such as 90days or 180 days. These predetermined time periods can be adjusted basedon the user's preferences. The color variances legend 119 illustratesthe displayed range. For example, as shown in FIG. 1H, the legend showsa color range from less than about −20% difference (production loss) tomore than about +20% difference (production gain). Null values(communication failures, no data, etc.) can be represented asnon-colored or gray boxes. In FIG. 1H, various cross-hatching patternsare again used to represent different colors. However, as previouslydiscussed, different colors, backgrounds, patterns, shades, textures, orother markings can be used to distinguish between different values ofattributes (e.g., percent changes in production).

FIGS. 2A and 28 illustrate descriptions or tooltips 105 for exampleartificial lift systems. In particular, FIG. 2A shows a tooltip 105 fora rod pump system and FIG. 2B shows a tooltip 105 for an ESP. In each ofthese embodiments, the tooltips can be activated by hovering over orselecting a particular artificial lift well in data body 101. Thetooltip 105 can provide production well information such asidentification information for the artificial lift system, productioninformation, test information, and equipment specifications. Forexample, identification information can include the artificial lift wellname, API14 (i.e., a government assigned 14 digit unique identifier fora well by the American Petroleum Institute), artificial lift type, fieldlocation and section location. Production information can include netproduction or a change in production (delta production) for oil, water,gas, or a combination thereof. Average production over predeterminedtime periods can also be displayed. In one embodiment, the predeterminedtime periods are set to a 30, 90, and 180 day average of production.These predetermined time periods can be adjusted based on the user'spreferences. Gross production, inferred production and flowline pressurecan also be displayed. Artificial lift well test information can includeoil cut, water cut, last test date, days since last test, and averagedays between tests.

As previously described, data body 101 can display individual artificiallift wells, a grouping of artificial lift wells, or both. By selectingan individual artificial lift well (e.g., left click of a computer mousewhile hovering over the artificial lift well), a navigation menu isdisplayed. The navigation menu includes options such as “zoom-in” and“zoom-out” functionality. The zoom-in option, drills-down the view inthe data body 101 to show a single hierarchy (e.g., section, gaugerbeat, manifold). In some embodiments, an artificial lift well countdisplays the number of wells for the selected or viewed section, gaugerbeat or manifold. The zoom-out option reverses the drill-down pathfollowed by the user.

The navigation menu also can include the option for tracking one or moreartificial lift wells or flagging one or more artificial lift wellsthrough multiple views. There is no limit on the number of wells thatcan be tracked or flagged. Additionally, flagged wells and tracked wellscan be used concurrently or independently of each other. Artificial liftwells being tracked are identified by a symbol within the artificiallift well box in the data body 101, such as a square check-box appearingwithin the artificial lift well box. Similarly, artificial lift wellsbeing flagged are identified by a separate symbol within the artificiallift well box in the data body 101, such as a flag icon appearing withinthe artificial lift well box. A screen display of all artificial liftwells that are tracked or flagged can be displayed by the user. Thetracking function is cleared each time users log out of the ArtificialLift Surveillance Tool. Accordingly, tracked wells will no longer betagged when the tool is closed and re-opened; whereas, the flaggingfunction is maintained when users log out of the Artificial LiltSurveillance Tool. Accordingly, flagged wells will reappear when thetool is closed and re-opened until the artificial lift well isunflagged.

Filters 107 allow users to reduce the number of wells displayed in thedata body 101, by setting upper and lower limits on key production andartificial lift metrics. Typically filters 107 are used to quicklyidentify potential artificial lift problems. For example, the tablesbelow list a number of filters that can be applied for rod pump systemsand electrical submersible pumps:

Production Filters Definition Well Test - % Oil Cut % of oil in theproduced fluid Well Test - % Water Cut % of water in the produced fluidDown Wells (# of days) Days the well has been down Flowline PressurePressure (psi) downstream at the wellhead

Equipment Filters for Rod Pump Systems Definition POC Run Time Delta(current % difference between the current run time (last 24 hours) vs.vs. 7 day average) 7 day run time average Delta Dyno Card Area %difference between card area of the current card vs. 7-day (current vs.7 day average) current card area average Cycle Count No. of starts/stopsin most recent 24 hour period Inferred Production vs. % differencebetween inferred production data and well test Metered Well Testproduction data Fluid Level Above Pump Fluid above the pump in feet PeakSurface Rod Load (lbs) Current vs. 7 day average of the Peak Surface RodLoad of vs. 7 day average the full card Pump Efficiency - Surface (%)The efficiency of the pump at the surface in percentage

Equipment Filters for ESPs Definition Yesterday's Runtime Currentruntime reported from the previous day Fluid Level Above Pump Fluidabove the pump in feet Phase A Amps - Daily % difference between thecurrent Phase A Amps (last 24 Average vs. Month's Average hours) vs. 30day Phase A Amp average Phase B Amps - Daily % difference between thecurrent Phase B Amps (last 24 Average vs. Month's Average hours) vs. 30day Phase R Amp average Phase C Amps - Daily % difference between thecurrent Phase C Amps (last 24 Average vs. Month's Average hours) vs. 30day Phase C Amp average Phase A Amps - Daily % difference between thecurrent Phase A Amps (last 24 Average vs. 3 Phase Average hours) vs. thecurrent 3 Phase average (Phase A, Phase B, Phase C) Phase B Amps - Daily% difference between the current Phase B Amps (last 24 Average vs. 3Phase Average hours) vs. the current 3 Phase average (Phase A, Phase B,Phase C) Phase C Amps - Daily % difference between the current Phase CAmps (last 24 Average vs. 3 Phase Average hours) vs. the current 3 Phaseaverage (Phase A, Phase B, Phase C) AB Volts - Daily Average vs. %difference between the current AB Volts (last 24 hours) Month's Averagevs. 30 day AB Volts average BC Volts - Daily Average vs. % differencebetween the current BC Volts (last 24 hours) Month's Average vs. 30 dayBC Volts average CA Volts - Daily Average vs. % difference between thecurrent CA Volts (last 24 hours) Month's Average vs. 30 day CA Voltsaverage AB Volts - Daily Average vs. % difference between the currentPhase AB Volts (last 24 Current 3 Leg Average hours) vs. the current 3leg average (AB volts, BC volts, CA volts) BC Volts - Daily Average vs.% difference between the current Phase BC Volts (last 24 Current 3 LegAverage hours) vs. the current 3 leg average (AR volts, RC volts, CAvolts) CA Volts - Daily Average vs. % difference between the currentPhase CA Volts (last 24 Current 3 Leg Average hours) vs. the current 3leg average (AB volts, BC volts, CA volts) Pump Intake Pressure Bottomhole pressure of the well

FIG. 3, according to an embodiment of the invention, illustrates slidingbar filters for a rod pump system that can be moved to alter the rangeof artificial lift wells displayed in data body 101. For example, as thebar is moved from each end, the data body set is reduced to only includeartificial lift wells with values that fall within the selected range.The specific values are updated as the bars move. In one embodiment, thespecific values being updated are displayed in text below the slidingbars. In one embodiment, the filters 107 remain in the selectedpositions in each view through zoom-ins and zoom-outs and are clearedeach time users log out of tool. In this case, filters 107 default tothe all open non-filtered position when the tool is re-opened.

The navigation menu also can include the option for users to quicklysearch and locate a specific well or a group of wells, such as from analphabetical listing of all artificial lift well names. FIG. 4illustrates an example of how artificial lift wells can be selected froma listing, such as an alphabetical listing, for viewing in the data body101. Previously searched groups of wells can also be saved in ALST. Forexample, the manage state button 121 (FIG. 1A) can be used to save,update, or delete a previously saved group of artificial lift wells. Thesaved groups of artificial wells can be accessed using the saved statedropdown list 125. Refresh button 127 can be used to return to a defaultsystem state where all artificial wells for an equipment type and assetare displayed. An export data button 123 can also be provided, such asin the header 103 to export a copy of the data being displayed in thedata body 101. For example, data can be exported into a database, suchas an Excel workbook, or printed to a hardcopy report for furtheranalysis. Users have the choice whether to export all the data withinthe tool or only the data set currently being displayed on the screen.Users may also export all tracked or flagged cells or only those trackedor flagged cells currently being displayed on the screen.

In one embodiment, a data quality function is provided such that datafalling out of normal or expected ranges is highlighted so that it caneasily be investigated to resolve data integrity problems. For example,a table showing a high level summary of data quality issues can bedisplayed. An example table is shown below:

Issue Count % Oil Cut Outside Threshold 2 % Water Cut Outside Threshold2 Cycle Count Outside Threshold 0 Delta Dyno Cart Outside Threshold 11Flow Line Pressure Outside Threshold 121 Fluid Level Outside Threshold 2Inferred Production vs. Meter Well Test Outside Threshold 85 POC RuntimeDelta Outside Threshold 11 Pump Efficiency Outside Threshold 12 Rod LoadDelta Outside Threshold 0 Values Out of Range 121In the above table, there are two artificial lift wells that have afluid level outside the expected threshold (i.e., the values selected inthe filters). A more detailed quality table can also be displayed toshow data quality issues for particular artificial lift wells. Forexample, the following table can be displayed:

Well Name Field API14 Out of Range Values XXX123 A 11223344556677 FlowLine Pressure, Inferred Production vs. Meter Well Test XXX234 B11223344556688 Flow Line Pressure XXX345 C 11223344556688 % Oil Cut, %Water Cut

The navigation menu can also include the option for launching theArtificial Lift Analysis Solution (ALAS), or other artificial liftsoftware, to further evaluate one or more selected artificial liftwells. Accordingly, production declines identified in ALST can beanalyzed in ALAS. For example, such production declines can often beattributed to several different potential problems with the artificiallift system. ALAS accesses production and artificial lift system data todetermine the root cause of problems and fully understand wellperformance improvement opportunities. In one embodiment, ALAS stores upto one (1) year of production and artificial lift system data. Inanother embodiment, ALAS stores up to two (2) years of production andartificial lift system data. In another embodiment, ALAS stores up tofive (5) years of production and artificial lift system data.

The Artificial Lift Analysis Solution can display all artificial liftwells or a sub-set of the artificial lift wells. For example, in oneembodiment, all artificial lift wells using the same artificial liftmethod (Equipment Type) are displayed. In one embodiment, all artificiallift wells located at the same asset (Operations Supervisor) aredisplayed. In one embodiment, a single well, which for example can belocated by API14, Completion Name, or Well Name, is displayed. As willbe described, the Artificial Lift Analysis Solution provides variousmodules including the dashboard module, Failure Predictions Module, welllisting module, alert history module, and neighborhood well modules. Insome embodiments, such as when analyzing rod pumping systems, a dyno(dynamometer) card listing module and a dyno (dynamometer) card historymodule can also be provided.

FIGS. 5A and 513 show an example of dashboard module 200. Dashboardmodule 200 provides users with failure alert summaries on all wellcompletions for each failure type (e.g., rod pump failure, tubingfailure, rod string failure). Data can be organized into several tablesin the dashboard module. Tables typically contain a list of artificiallift wells or other artificial lift well related information. Selectingan item in a table typically populates other areas within the tool.Tables are typically sort-able by clicking on any column header withinthe list.

In FIG. 5A, the “Open Alerts” table 201 of the dashboard module 200provides users with a summary of the failure alerts that have eitherbeen detected or predicted. ALAS has an Artificial Intelligence (AI)engine and alerts are sent daily based on the predictive analysis. Sincealerts are not resolved immediately, daily alerts may be sent for thesame issue. To eliminate “repeated” alerts, the system rolls-up thealerts into an alert summary, thus increasing the count of alerts for agiven artificial lift well instead of displaying the alert for theartificial lift well multiple times. Detected failures are depicted as“failure detected” in the “urgency” column, while predicted failures aredepicted as “watchlist.” As used herein, the terms “detected failure”and “failure detected” are defined as forecasted artificial lift systemfailures eminent within 14 days, while “predicted failure” and “failurepredicted” are defined as forecasted artificial lift system failuresthat can occur greater than 14 days way, such as between 14 days and 100days.

Examples of methods that can be used for detecting failures aredisclosed in U.S. provisional patent application bearing Ser. No.61/500,325, filed on Jun. 23, 2011, which is incorporated herein byreference in its entirety. Examples of method that can be used forpredicting failures are disclosed in United States non-provisionalpatent application bearing Ser. No. 13/118,067, filed on May 27, 2011,and United States non-provisional patent application bearing Ser. No.13/330,895, filed on Dec. 20, 2011, both of which are incorporatedherein by reference in their entirety.

In one embodiment of the present invention, the “Open Alerts” table 201is displayed based on a default sorting. For example, alerts can bedefaulted to be sorted first according to urgency, then by rank, andthen by status. With regards to urgency, the highest level of urgency is“failure detected.” The lower and next level of urgency is the“watchlist.” Rank is used to sort by normalizing the average dailyproduction and potential production volume loss. Ranking can be based ona scale (e.g., a 100 point scale) where a higher rank correlates to ahigher producer with higher potential volume loss. An artificial liftwell's priority can change daily based on the productivity loss for theartificial lift wells that have open alerts each day. The status columncan be sorted according to “new” alerts, “in evaluation” alerts, “inprogress” alerts, and “closed” alerts. All alerts are included in thedaily ranking until they have a status of closed. In some embodiments, asystem priority can be assigned to open alerts (i.e., new, inevaluation, or in progress). For example, a “high” priority can beassigned to the top 40% of the alerts, a “medium” priority can beassigned to the middle 40% of the alerts, and a “low” priority can beassigned to the bottom 20% of the alerts. The artificial lift wells canfurther be color-coded based on the system priority when the alerts aredisplayed to the user. For example, artificial lift wells having a highpriority can be colored red, artificial lift wells having a mediumpriority can be colored orange, and artificial lift wells having a lowpriority can be colored yellow. Furthermore, normal wells having nopredicted or detected failures can be colored green. As previouslydiscussed, different backgrounds, patterns, shades, textures, or othermarkings can alternatively be used. The user can also sort data (i.e.,information sorted under column headers) in ascending or descendingorder based on any column of interest.

In one embodiment, the Artificial Lift Analysis Solution (ALAS) hasbuttons 203 at the top of the screen on the dashboard module 200. Forexample, buttons 203 can include an “Analyze Selected Alert” button, a“Sort by Defaults” button, a “Sort by Status” button, a “Sort by SystemPriority” button, and a “Sort by User Priority” button. The “AnalyzeSelected Alert” button takes the user to the Failure Predictions Moduleand displays artificial lift wells for further evaluation that wereselected by a user in the dashboard module 200. As will be described, itprovides the artificial lift well's open alerts, history trend and alerthistory. In addition, the artificial lift well associated with theselected alert is also placed in other ALAS modules for furtheranalysis. The “Sort by Defaults” button, resets the sort order back tothe system defaults. For example, the “Sort by Defaults” button can bebased on immediacy of failure (urgency) and potential production volumeloss (rank). The “Sort by Status” button sorts open alerts in the orderof “new” alerts (alerts not previously reported), “in evaluation” alerts(confirmation and evaluation of the alert is being performed), “inprogress” alerts (appropriate personnel have been notified of the alertor remediation is in progress), and “closed” alerts (work has beencompleted or the alert has been identified as a false positive andreturned for retraining). Alternately, clicking on the “Status” columnheader will sort open alerts alphabetically (i.e., closed, inevaluation, in progress and new). In one embodiment, the “Sort by SystemPriority” button sorts open alerts in the order of high, medium and lowpriority. In another embodiment, clicking on the “Sort by SystemPriority” column header will sort open alerts alphabetically (i.e.,high, low and medium). The “Sort by User Priority” button sorts openalerts in an order established by the user. Clicking any of the buttons203 a second time can reverse the order items are sorted in.

FIG. 5B shows the Open Alerts Filter Panel 205, which allows the user tobetter manage the “Open Alerts” table 201. The initial display is basedon default filter settings. The user may at any time change the settingsof one or more filter(s). The selected view will remain until the userchanges the filters or closes the application. When the application isreopened, the filters will return to the default settings. In oneembodiment “slider” filters are used to manage the “Open Alerts” table201. Slider filters allow the user to quickly indicate a range of valuesfor one or more attributes. For example, slider filters are used forrank and duration in FIG. 5B. Here, the minimum (0 for rank, 1 forduration) and maximum (100 for rank, 519 for duration) are automaticallypopulated based on the dataset provided. In another embodiment,“checkbox” filters are used to manage the “Open Alerts” table 201.Checkbox filters allow the user to select or deselect one or more valuesof each attribute. For example, checkbox filters are used in FIG. 5B forstatus, urgency, user priority, and system priority.

Visualization window 207 (FIG. 5A) is provided to display changes in the“Open Alerts” table 201. For example, visualization window 207 caninclude a pie chart of open alerts by failure type (e.g. rod pumpfailures, tubing failures). In some embodiments, a percent ratio andcount of each failure type can also be displayed in visualization window207. Visualization window 207 can also include a pie chart of othercolumn information. For example, FIG. 5A shows a pie chart of openalerts by system priority (i.e., high, low and medium). Data canalternatively be displayed in visualization window 207 using othercommon graphics such as bar or line graphs.

In “Open Alerts” table 201, the user has the ability to select any ofthe alerts displayed. Here, the user can click on the “Analyze SelectedAlert” button 203 to further analyze any of the alerts. The “AnalyzeSelected Alert” button takes users to the Failure Predictions Modulewhere historical data trends can be viewed and further analysis can beperformed. As previously discussed, the Failure Predictions Module canalso be accessed directly by clicking on the Failure Predictions tab inALAS.

FIGS. 6A and 6B show an example of Failure Predictions Module 300. Aswill be described, Failure Predictions Module 300 can be used to view abreakdown of alert summaries, view history trend graphs for identifiedwells and view alert history graphs that depict detection and predictionalert occurrences for selected wells by alert type. In FIG. 6A, the nameof the artificial lift well and an associated alert priority indicator,which represents the highest priority alert for the selected well suchas by color or shading, are displayed at the top of the page. In oneembodiment, Failure Predictions Module 300 includes four visualizationsand a filter panel 301. The visualizations are well listing 303 (FIG.6A), alerts for selected completions 305 (FIG. 6A), well history trend307 (FIG. 6B), and alert history 309 (FIG. 6B).

Filter Panel 301, which is similar to filters 205 of dashboard 200,allows the user to manage the open alerts appearing in thevisualizations of Failure Predictions Module 300. The initial display isbased on default filter settings; however, the user may at any timechange the settings of one or more filter(s). The selected view willremain until the user changes the filters or closes the application.When the application is reopened, the filters will return to the defaultsettings.

The visualization for well listing 303 displays information on allartificial lift wells whether or not they have alerts. The user mayselect any listed artificial lift well on this table for furtheranalysis. When the well selection is changed, the newly selectedartificial lift well is highlighted. The information displayedthroughout the Failure Predictions Module 300 automatically updates toreflect the selected artificial lift well(s). For example, if analerting well is selected, the alerts for the selected well will bepopulated and displayed in the visualization for alerts for selectedcompletions 305. If the well chosen has no alerts, no alerts will bepopulated in the visualization for alerts for selected completions 305.If a well is not tagged as having an alert but the user knows of analert, an alert can be created and saved for that well.

The visualization for alerts for selected completions 305 contains alist of the alerts for wells selected in well listing 303. Alerts forselected completions 305 include a duration attribute, which depicts thenumber of days since the first alert for that specific failure wassurfaced by the artificial intelligence engine. The duration attributecontinues to count the number of days until the alert is closed. Thealerts displayed can be ordered based on failure type, status, systempriority, or user priority.

Alerts can be managed by selecting an alert in either the visualizationsfor well listing 303 or the alerts for selected completions 305. Forexample, to manage an alert the user can select the desired completion,such as by performing a right click with a mouse, to bring up a pop-upmenu. “Manage Alert” can be selected in this menu, which brings up adialog box where the user will be able to change the status, enter thealert close date, add comments, return the alert for retraining, and/orsend an editable email containing a link for the selected alert. Theuser will also be able to “create an alert” if a failure occurs that wasnot detected or predicted by the artificial intelligence engine. At anytime a failure is identified in the field that should have been detectedor predicted, the user is able to use the manage alert pop-up to createan alert and have it returned to the artificial intelligence engine forretraining. The ability to manage alerts can be restricted to subjectmatter experts or power users of ALAS if desired.

FIGS. 7A and 7B show examples of manage alert pop-ups. A new alert canbe either a good alert, a false alert, or it can reflect an actual alertbut the wrong failure type. When an alert is a false alert, the user cancheck the “Return for retraining” box and identify the actual failuretype as none. If the status is set to “In Evaluation,” as in FIG. 7A,the system will continue to track similar alerts under the same alertsummary. If the status is set to “Closed,” as in FIG. 7B, the systemwill generate a new alert summary for next alert received. When an alertindicates the wrong failure type, the user can check the “Return forretraining” box, select the correct failure type, update the “Status” ofthe artificial lift well, and add comments as appropriate to keep thealert flowing through the alert process.

The visualization for well history trend 307 (FIG. 6B) is a graphdisplaying trend data for a selected well. In one embodiment, the graphincludes 1 year of artificial lift data. In another embodiment, thegraph includes 2 years of artificial lift data. Viewing the data in thisformat can help to quickly identify changes in key artificial liftmetrics. The trend data also helps to tie several metrics together. Forexample, if water production was increasing while oil production wasdecreasing and rod load was increasing, a problem with the pump isprobable. The user may add and/or remove trending parameters from adropdown menu that is inherent to the visualization section. Moreover,the dropdown menu can be defaulted to list trending parameters for theselected well. For example, for rod pump wells the trend graph candefault to net oil, net gas, net water, peak surface load full, and cardarea current. For ESP Wells, the trend graph can default to net oil, netgas, net water, yesterday's runtime, daily motor temperature, pumpoutput frequency, and pump intake pressure. The changes to the databeing trended are displayed during the user's current session andtypically return to the default data when the user's session is closed.

The visualization for alert history 309, which is shown in FIG. 6B, is agraph of the each alert that has been surfaced by the artificialintelligence engine. For example, the graph can show each type of alert,the day it was surfaced, and the failure type for the selected well. Thedata used to populate well history trend 307 and alert history 309 canbe retrieved from one or more databases or information storage systemsuch as a System of Records (SOR), DataMart™, LOWIS™ (Life of WellInformation Software), or Microsoft® Access. The graphs for both thewell history trend 307 and alert history 309 display time on the x-axis.The period investigated can be changed by the user to alter the displayas desired. In one embodiment, the two graphs are synchronized such thatan adjustment to time on one graph is reflected in the other graph.Additional information about a data point can be displayed by hoveringover the data point. For example, this information can includeidentifying information for a selected well, including OperationsSupervisor, Gauger Beat Name, Field Name, API14, Lift Type, ProducingMethod and Down Days.

Each data point on the well history trend 307 and alert history 309 isassociated with a point in history for the well. Typically well testdata associated with that data point can be displayed. Productioninformation, which is calculated between well tests taking into accountany downtime during the period, can also be displayed. Productioninformation can include the current oil, water and gas production, alongwith the average production for a period of time, such as over 30 days,60 days, 90 days, or 6 months. This well test data and productioninformation can be used to analyze the presence of any issues. Forexample, a decline in production for the well could be observedindicating that a change may need to be made to the artificial liftsystem. Historical equipment changes can also be tracked, which can beused to tie a performance problem to a recent equipment change (or lackthereof). For example, the timing of a recent job can be compared to adecrease in production to eliminate the possibility of a pump settingchange as the cause of the production decline.

According to one embodiment, information associated with surroundingartificial lift wells of a selected artificial lift well can be reviewedby selecting the “Neighborhood Wells” module in ALAS. In particular, theNeighborhood Wells module provides detailed data, including productionsummary data and production trends, for producer and injector wellswithin the same region (e.g., a certain radial distance) of a selectedwell. This information can be used to identify any potential productioninterference between wells within the same region. Accordingly, theselected well data can be visually compared to neighboring well data toevaluate commonalities/anomalies and thus determine whether issues areisolated to an individual well or if they are more wide spread. Forexample, well data can be compared by Operations Supervisor, GaugerBeat, Field Name, Equipment Type, Lift Type, Producing Method and DownDays. An example screenshot of the Neighborhood Wells module is shown inFIGS. 8A and 8B.

In one embodiment, a dyno card well listing module is provided in ALAS.The dyno card well listing module displays the results of ArtificialIntelligence, such as an Artificial Neural Network (ANN), and rule-based(based on subject matter expert knowledge) analysis, which identifiescards that may be having several common pump problems. For example, thedyno card well listing can display well names, basic equipment,production data and any issues that the tool predicted in the cards suchas pump fillage, friction or POC problems. The results from the dynocard well listing module can be utilized to quickly identify cards thatrequire further investigation. The following table shows examples ofdyno cards that can be utilized to analyze pump issues:

Card Type When is this card used? Results Current Available when well isnot cycling. POC Problem Card Fillage Tubing Movement Leak Full CardAvailable when well is cycling. POC Problem Fillage Tubing Movement LeakPump Off Available when well is cycling. Typically POC Problem Card usedto predict issues with Pump Off Fillage Fillage and Friction or whencurrent Tubing Movement and full cards are not available. Leak POFillage Friction

FIG. 9 shows an example of a dyno card well listing provided in ALAS.The following logic can be used to populate the dyno card well listing:

-   -   Does the card appear to be collapsed?        -   If true, “Yes” is indicated in the “Collapsed Card” column    -   Does the card indicate that there is an outflow issue?        -   If true, “Yes” is indicated in the “Outflow” column. Outflow            issues can be assessed using a formula that looks at fillage            for variable speed drive (VSD) pumps and fluid level for            non-VSD pumps.    -   Is there a pump-off controller (POC) issue?        -   If true, “Problem” is indicated in the “POC” column. Here,            no pump issues can be analyzed because the data is not            reliable and “N/A” will appear for other columns.    -   is there a tillage issue?        -   The tool will indicate the card's percent fillage in the            “Fillage” column.            -   Fillage is typically assessed only when there is a valid                card.    -   Is there friction?        -   If true, “Yes” is indicated in the “friction” column. In one            embodiment, friction is assessed only when there is a valid            pump off card that is less than 90% full.    -   Is there tubing anchor movement?        -   If true, “Yes” is indicated in the “tubing anchor movement”            column. In one embodiment, tubing anchor movement is            assessed only when there is a valid card that is more than            90% full.    -   Is there a leak?        -   Leaks are typically assessed only when there is a valid            card.

The dyno card well listing can also be filtered based on basic well data(e.g., API14, well name, field name, section, gauger beat, gauger beatnumber, prediction conflict, card date, card type. POC mode, invertedcard and VSD), heuristics data coming directly from the well (e.g.,collapsed card and outflow), diagnostics of most common pump problems(e.g., POC problems, linage issues, friction, tubing movement, leak),well status including the number of days a well has been down and downcode (e.g., DH (hole in tubing), DO (other downhole problems), DP (pumpfailure), LE (electrical equipment problem), LO (other surface problem),LW (pumping unit problem), OL (pipeline/flowline leak), OS (stimulation,e.g., hydraulic fracture)), production data (e.g., gross production, netoil, production date, runtime), or a combination thereof. Use of thesefilters help narrow down the wells such that only wells that may have agiven issue are displayed.

In one embodiment, a dyno card history module is provided in ALAS. Thedyno card history module displays historical dyno cards for the selectedwell. For example, in one embodiment, 16 historical dyno cards aredisplayed. Here, the first 8 cards represent the most current eightdaily readings. The next 4 cards display readings at approximate weeklyintervals taking the first available card for each week over a four weekperiod. The last 4 cards display monthly (4 week) intervals taking thefirst available card from the first week of each four week period. Thishistorical data can help to quickly identify key changes in the dynocards and pinpoint when an issue may have begun. FIG. 10 shows anexample of a dyno card history for an artificial lift well.

All information in ALAS can be exported. For example, data can beexported into a database, such as an Excel workbook, or printed to ahardcopy report for further analysis. Information can also be exportedto PowerPoint for use in a future presentation. Alerts can further bepassed to the Artificial Lift Management Tool (ALMT).

In one embodiment, an Artificial Lift Management Tool (ALMT) is used toreduce the time needed to respond to artificial lift well failures inthe field, thereby reducing downtime and lost production due toinefficiencies. The Artificial Lift Management Tool serves as acollaborative tool allowing operations control personnel in the field totrack artificial lift well work from the time a failure alert issurfaced and a ticket is opened to the time the ticket is closed.Automatic alerts for exceptions are circulated to designated fieldpersonnel using artificial intelligence. Such alerts include specificinstructions with regards to handling the alert. If alerts are notreviewed and/or closed by the designated field personnel within apredetermined time period (e.g., 1 day, 1 week), the alerts can bererouted to an alternate contact, such as the field personnel'ssupervisor. ALMT can also prioritize alerts to be handled, such as byeach artificial lift well's production volumes and/or percentageproduction loss. Accordingly, field personnel can follow the processfrom exception identification until the alert has been closed. TheArtificial Lift Management Tool enables different levels of users toaccess information on the completion level (% of actions completed,personnel assigned, days since alert, etc.) of defined tasks, which canimprove the rigor around management and closure of action items.Resolutions to alerts are tracked and tied back to a knowledge base,which can later be used for recommending solutions for future alerts.Tracking can further be used to report performance with regards tohandling alerts to field management teams.

Those skilled in the art will appreciate that the above describedmethods may be practiced using any one or a combination of computerprocessing system configurations, including, but not limited to, singleand multi-processor systems, hand-held devices, programmable consumerelectronics, mini-computers, or mainframe computers. The above describedmethods may also be practiced in distributed or parallel computingenvironments where tasks are performed by servers or other processingdevices that are linked through one or more data communicationsnetworks. For example, large computational problems can be broken downinto smaller ones such that they can be solved concurrently—or inparallel. In particular, the system can include a cluster of severalstand-alone computers. Each stand-alone computer can comprise a singlecore or multiple core microprocessors that are networked through a huband switch to a controller computer and network server. An optimalnumber of individual processors can then be selected for a givenproblem.

As will be described, the invention can be implemented in numerous ways,including for example as a method (including a computer-implementedmethod), a system (including a computer processing system), anapparatus, a computer readable medium, a computer program product, agraphical user interface, a web portal, or a data structure tangiblyfixed in a computer readable memory. Several embodiments of the presentinvention are discussed below. The appended drawings illustrate onlytypical embodiments of the present invention and therefore, are not tobe considered limiting of its scope and breadth.

FIG. 1I illustrates an example computer system 400 for surveillance,analysis, and management of artificial lift systems, such as by usingthe methods and tools previously described herein. System 400 includesuser interface 410, such that an operator can actively input informationand review operations of system 400. User interface 410 can be any meansin which a person is capable of interacting with system 400 such as akeyboard, mouse, or touch-screen display. In some embodiments, userinterface 410 embodies spatial computing technologies, which typicallyrely on multiple core processors, parallel programming, and cloudservices to produce a virtual world in which hand gestures and voicecommands are used to manage system inputs and outputs.

Operator-entered data input into system 400 through user interface 410,can be stored in database 430. Measured artificial lift system data,such as from POCs, which is received by one or more artificial liftsystem sensors 420, can also be input into system 400 for storage indatabase 430. Additionally, any information generated by system 400 canalso be stored in database 430. Accordingly, database 430 can storeuser-defined parameters, measured parameters, as well as, systemgenerated computed solutions. Database 430 can store, for example,artificial lift, systems sensor measurements 431, which are indicativeof operational statuses of artificial lift systems, obtained throughload cells, motor sensors, pressure transducers and relays. Datarecorded by artificial lift system sensors 420 can include, for example,surface card area, peak surface load, minimum surface load, strokes perminute, surface stroke length, flow line pressure, pump fillage,yesterday cycles, and daily run time. Furthermore, gearbox torque,polished rod horse power, and net pump efficiency can be calculated forstorage in database 430. Artificial lift system test data 433, which caninclude last approved oil, last approved water, and fluid level, canalso be stored in database 430.

System 400 includes software or computer program 440 that is stored on anon-transitory computer usable or processor readable medium. Currentexamples of such non-transitory processor readable medium include, butare not limited to, read-only memory (ROM) devices, random access memory(RAM) devices and semiconductor-based memory devices. This includesflash memory devices, programmable ROM (PROM) devices, erasableprogrammable ROM (EPROM) devices, electrically erasable programmable ROM(EEPROM) devices, dynamic RAM (DRAM) devices, static RAM (SRAM) devices,magnetic storage devices (e.g., floppy disks, hard disks), optical disks(e.g., compact disks (CD-ROMs)), and integrated circuits. Non-transitorymedium can be transportable such that the one or more computer programs(i.e., a plurality of instructions) stored thereon can be loaded onto acomputer resource such that when executed on the one or more computersor processors, performs the aforementioned functions of the variousembodiments of the present invention.

Computer program 440 is used to perform any of the steps or methodsdescribed herein. In some embodiments, computer program 440 is incommunication (such as over communications network 470) with otherdevices configured to perform the steps or methods described herein.Examples of computer program 440 include, but are not limited to,Artificial Lift Surveillance Tool (ALST) 441, Artificial Lift AnalysisSolution (ALAS) 443, and Artificial Lift Management Tool (ALMT) 445.

Processor 450 interprets instructions or program code encoded on thenon-transitory medium to execute computer program 440, as well as,generates automatic instructions to execute computer program 440 forsystem 400 responsive to predetermined conditions. Instructions fromboth user interface 410 and computer program 440 are processed byprocessor 450 for operation of system 400. In some embodiments, aplurality of processors 450 is utilized such that system operations canbe executed more rapidly.

In certain embodiments, system 400 includes reporting unit 460 toprovide information to the operator or to other systems (not shown). Forexample, reporting unit 460 can provide alerts to an operator ortechnician that an artificial lift system is predicted or detected tofail. The alert can be utilized to minimize downtime of the artificiallift system or for other reservoir management decisions. Reporting unit460 can be a printer, display screen, graphical user interface (GUI), ora data storage device. However, it should be understood that system 400need not include reporting unit 460, and alternatively user interface410 can be utilized for reporting information of system 400 to theoperator or another designated person.

Communication between any components of system 400, such as userinterface 410, artificial lift system sensors 420, database 430,computer program 440, processor 450 and reporting unit 460, can betransferred over communications network 470. Computer system 400 can belinked or connected to other, remote computer systems or measurementdevices (e.g., POCs) via communications network 470. Communicationsnetwork 470 can be any means that allows for information transfer tofacilitate sharing of knowledge and resources, and can utilize anycommunications protocol such as the Transmission ControlProtocol/Internet. Protocol (TCP/IP). Examples of communications network470 include, but are not limited to, personal area networks (PANs),local area networks (LANs), wide area networks (WANs), campus areanetworks (CANs), and virtual private networks (VPNs). Communicationsnetwork 470 can also include any hardware technology or equipment usedto connect individual devices in the network, such as by wiredtechnologies (e.g., twisted pair cables, co-axial cables, opticalcables) or wireless technologies (e.g., radio waves).

Many modifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific examples described herein are offeredby way of example only, and the invention is to be limited only by theterms of the appended claims, along with the full scope of equivalentsto which such claims are entitled.

As used in this specification and the following claims, the terms“comprise” (as well as forms, derivatives, or variations thereof, suchas “comprising” and “comprises”) and “include” (as well as forms,derivatives, or variations thereof, such as “including” and “includes”)are inclusive (i.e., open-ended) and do not exclude additional elementsor steps. Accordingly, these terms are intended to not only cover therecited element(s) or step(s), but may also include other elements orsteps not expressly recited. Furthermore, as used herein, the use of theterms “a” or “an” when used in conjunction with an element may mean“one,” but it is also consistent with the meaning of “one or more,” “atleast one,” and “one or more than one.” Therefore, an element precededby “a” or “an” does not, without more constraints, preclude theexistence of additional identical elements.

What is claimed is:
 1. A method for artificial lift system analysis, themethod comprising: (a) providing production well information for aplurality of the production wells each being associated with anartificial lift system; (b) receiving and processing, on a computer,artificial lift system failure alerts for the plurality of productionwells; (c) determining a relevance measure for each of the artificiallift system failure alerts responsive to the production wellinformation; and (d) displaying a summary of the artificial lift systemfailure alerts in an ordering based on the relevance measure.
 2. Themethod of claim 1, wherein: the artificial lift system failure alertscomprise predicted failure alerts and detected failure alerts; and therelevance measure comprises an urgency of the artificial lift systemfailure alerts such that the detected failure alerts are ordered beforethe predicted failure alerts in the summary of the artificial liftsystem failure alerts.
 3. The method of claim 1, wherein the relevancemeasure comprises a combination of average daily production andpotential production volume loss for each of the plurality of productionwells.
 4. The method of claim 1, wherein the relevance measure comprisesan artificial lift failure type.
 5. The method of claim 1, wherein therelevance measure comprises a completion status such that the artificiallift system failure alerts are ordered according to whether the alert isassociated with a new, under evaluation, in progress, or closedcompletion status.
 6. The method of claim 1, further comprising: (e)selecting one or more of the plurality of production wells; and (f)generating a well history graph for the one or more of the plurality ofproduction wells selected.
 7. The method of claim 1, further comprising:(e) selecting one or more of the plurality of production wells; and (f)generating an alert history graph for the one or more of the pluralityof production wells selected.
 8. The method of claim 1, furthercomprising: (e) selecting one or more of the plurality of productionwells; and (f) generating a comparison of production performance for theone or more of the plurality of production wells selected with one ormore associated neighborhood wells.
 9. The method of claim 1, furthercomprising: (e) identifying an artificial lift system failure alert thatis incorrect; (f) correcting the artificial lift system failure alert;and (g) training an artificial intelligence engine with the correctedartificial lift system failure alert.
 10. The method of claim 1, furthercomprising: (e) selecting one or more of the plurality of productionwells that are associated with rod pump artificial lift systems; and (f)generating a dynamometer card history for the one or more of theplurality of production wells selected.
 11. A system for artificial liftsystem analysis, the system comprising: a database configured to storeproduction well information for a plurality of production wells eachbeing associated with an artificial lift system; a computer processor; acomputer program executable on the computer processor, the computerprogram comprising an Artificial Lift Analysis Solution that: (a)processes artificial lift system failure alerts for the plurality ofproduction wells; (b) determines a relevance measure for each of theartificial lift system failure alerts responsive to the production wellinformation; and (c) orders the artificial lift system failure alertsusing the relevance measure; and a display that communicates with thecomputer program to produce image data representations of the artificiallift system failure alerts ordered using the relevance measure.
 12. Thesystem of claim 11, wherein: the artificial lift system failure alertscomprise predicted failure alerts and detected failure alerts; and therelevance measure comprises an urgency of the artificial lift systemfailure alerts such that the detected failure alerts are ordered beforethe predicted failure alerts.
 13. The system of claim 11, wherein therelevance measure comprises a combination of average daily productionand potential production volume loss for each of the plurality ofproduction wells.
 14. The system of claim 11, wherein the ArtificialLift Analysis Solution further comprises a well history module thatgenerates a well history graph for one or more of the plurality ofproduction wells.
 15. The system of claim 11, wherein the ArtificialLift Analysis Solution further comprises an alert history module thatgenerates an alert history graph for one or more of the plurality ofproduction wells.
 16. The system of claim 11, wherein the ArtificialLift Analysis Solution further comprises a well neighborhood module thatgenerates a comparison of production performance for one or more of theplurality of production wells with one or more associated neighborhoodwells.
 17. The system of claim 1 wherein the Artificial Lift AnalysisSolution further comprises a failure alerts management module thatcorrects artificial lift system failure alerts to train an artificialintelligence engine.
 18. The system of claim 11, wherein the ArtificialLift Analysis Solution further comprises a dynamometer card historymodule that generates a dynamometer card history for one or more of theplurality of production wells associated with rod pump artificial liftsystems.
 19. A non-transitory processor readable medium containingcomputer readable software instructions used for surveillance ofartificial lift systems, the computer readable instructions comprising:(a) processing production well information and artificial lift systemfailure alerts for a plurality of production wells each being associatedwith an artificial lift system; (b) determining a relevance measure foreach of the artificial lift system failure alerts responsive to theproduction well information; and (c) ordering the artificial lift systemfailure alerts using the relevance measure.
 20. The non-transitoryprocessor readable medium of claim 19, wherein: the artificial liftsystem failure alerts comprise predicted failure alerts and detectedfailure alerts; and the relevance measure comprises an urgency of theartificial lift system failure alerts such that the detected failurealerts are ordered before the predicted failure alerts.